Finally, experiments conducted on real-world data sets demonstrate the interest of considering both elliptical distributions and factor model structures in a graph learning process.
We observe that the proposed algorithms offer better interpretability of the obtained graphs compared to unstructured models.
Notably, the factor model approaches compare well with the current state-of-the-art on Laplacian-constrained graph learning method that can set the number of components as additional prior information [1]}, [2]}.
